Below Puneet Taneja, Head of Operations at Teleperformance, discusses with Finance Monthly how banks can prevent, detect and protect against fraud.

Trade body UK Finance reports that over £500 million was lost to fraud in the first half of 2018. What is particularly worrying is that of the £500 million lost to fraud, over £385 million was lost with no knowledge or authorisation from the account holder¹.

This news seems to cement current fears that fraudsters are becoming increasingly more sophisticated in their efforts to rob banking customers and overcome current financial security and anti-fraud measures. The rise of cybercrime has led to a new generation of fraudsters using technology to come up with new and innovative ways to steal hundreds of millions of pounds from customers, all while remaining undetected.

Although this may be stating the obvious, identifying, investigating and ultimately preventing fraud must continue to be a high priority. When banks consider the technology implementation necessary to drive banking innovation forward, this initiative is still in its infancy, with banks always striving to be on top of the latest and most effective methods to overcome fraudulent activity.

A reassessment of banking technologies and systems is the key to safeguarding customer accounts.

It’s all well and good to harness the power of existing technologies and data analytics to spot irregular data patterns to highlight suspicious transactions but this is only half the story. Employing a greater number of customer service agents who can aid in the risk management process can similarly help banks pre-empt fraud and treat the causes of financial loss, as opposed to the symptoms.

Overcoming fraudulent losses has the natural flow-on effect of boosting customer satisfaction, one of the key factors to banks’ long-term financial health. If customers view banks as being up to date on the relevant technologies to keep on top of inbound fraud, reputational equity builds and so too does customer satisfaction. This relies on banks being able to tackle the issue of fraudulent transactions in real time, in a proactive manner, rather than taking a reactive approach.

Using real-time anomaly techniques to spot suspicious transactions, financial institutions can achieve an astounding 92 percent reduction in fraud losses; in one instance, a UK national bank saved £3.54 million annually from credit and debit card fraud by using analytics technology.

Not only are banks being able to mitigate the financial consequences but also the reputational repercussions from those who have fallen victim. Naturally, it can be very damaging to any organisations reputation when the media publishes an incident involving fraud. Banks need to ensure that customers appreciate the back-office efforts that are put into place to not only prevent fraud, but also support customers who fall victim to fraud.

Nevertheless, fraud is an inescapable risk associated with performing financial services and banks have a responsibility to be well prepared on how they respond to fraudulent activity. From a customer services standpoint, the main driver of this preparedness comes from banks needing to be seen as being on the customer’s side. This concerns being prepared to help consumers through financially troublesome times, like when they fall prey to fraudulent activity. This is an integral part of banks’ customer service efforts.

Overcoming fraud is a nation-wide effort that every organisation in the industry is currently attempting to accomplish. Eliminating fraudulent activity altogether may not yet be possible but firms have the technology available to make a significant difference. Considering a fraud prevention systems overhaul may the key driver to banks detecting fraud faster and more efficiently than in recent times.

On the back of Deutsche Bank’s recent ordeal, Finance Monthly gets the lowdown from Zac Cohen, General Manager at Trulioo, who discusses the steps banks and other financial institutions can take to strengthen their fight against money laundering.

Deutsche Bank recently made headlines after the German financial watchdog BaFin appointed an independent auditor to monitor the bank’s Anti Money Laundering (AML) compliance. This is the first time such an appointment has been implemented, highlighting the bank’s failure to meet due diligence requirements surrounding terrorist financing, money laundering and other illicit flows of capital.

As banks and financial organisations now operate in an increasingly global marketplace, they must grapple with the consequences of handling cross border transactions. Having lax Know Your Customer (KYC) procedures in place can be potentially crippling for banks worldwide, with fines being issued in the hundreds of millions if chinks in their anti-money laundering armour are uncovered.¹ Yet despite over $20 billion being spent on compliance annually, only 1 per cent of illicit transactions are seized each year.²

Financial globalisation, still very much a reality despite shifting geo-political attitudes towards it, makes international money laundering practices a real force to be reckoned with. Indeed, international money laundering is becoming more widespread and this is, in part, down to the difficulties in maintaining full transparency when dealing with international clientele.

Banks and other financial institutions are legislatively obliged under Anti-Money Laundering rules to have full knowledge over their clients’ identities and the origins of their wealth. With money coming in from all corners of the globe, banks must be able to perform Know Your Customer (KYC) and Know Your Business (KYB) checks on a client base that may be moving money all around the world. In addition, establishing a “beneficial owner”, a derivative of KYC, must be a priority before financial transactions occur. The 4^th Anti Money Laundering Directive (4AMLD) stipulates the necessity of ascertaining the beneficial owner of business customers, partners, suppliers and other business stakeholders. Some transactions, originating from unknown geographic localities, can be particularly difficult to verify.

The key to combatting this problem is leveraging the available technologies that can be implemented to help promote transparency. This is crucial as these technologies have the view to reducing the occurrence of fraudulent transactions passing through banks and financial institutions. Bad actors are becoming increasingly sophisticated in their techniques in directing fraudulent money through banks, employing techniques such as under- or over-invoicing, falsifying documents, and misrepresenting financial transactions. This increasing sophistication that coincides with the rise in global money laundering, up 12 per cent from the previous year.³

There are however, multiple technical advances that are available to help implement and streamline the process of checking and verifying ultimate beneficial owners and promoting transparency. Automated systems and artificial intelligence programmes can be used to scour company documents for a streamlined electronic ID verification sytems to verify personally identifiable information in conjunction with ID document verification and facial recognition technology to help paint a full picture of each beneficial owner of a business.

Putting this all together to create certainty and transparency about who you’re doing business with is crucial. Deutsche Bank have suffered severe reputational damage as a result of several anti-money laundering breaches that have reached the public’s attention over the last few years. The question remains, can banks implement the technology and processes they need with sufficient effectiveness to recover from this reputational strain?

1 https://www.reuters.com/article/us-deutsche-bank-moneylaundering-exclusi/exclusive-deutsche-bank-reports-show-chinks-in-money-laundering-armor-idUSKBN1KO0ZC

2 https://www.politico.eu/article/europe-money-laundering-is-losing-the-fight-against-dirty-money-europol-crime-rob-wainwright/

3 https://www.pwc.com/gx/en/services/advisory/forensics/economic-crime-survey.html

Creating a balanced and even workflow will optimise productivity for robots – in the same way as it will for human workers.

Surely robots don’t get tired, can work 24/7, are fully skilled at what they are programmed to do, and don’t have any pesky motivational issues – so their productivity must always be consistently high? Absolutely not. This is according to Neil Bentley, Non-Executive Director & Co-Founder of ActiveOps, a leading provider of digital operations management solutions.

To believe this would be to forget everything we have learned about Lean Workflow and the way production systems work. For a processor (robot or human) productivity is best measured as a ratio of output:input. How much work did we get out for the amount of time we put in? For this to make sense we generally convert time into “capacity to do work” based on some idea of how much work could be done in a given time.

So, if Person A completes 75 tasks in a day and they had capacity to complete 100 then their productivity was 75%. Similarly, if Robot B completes 500 tasks in a day and had capacity to do 1,000 then their productivity would be 50%.

As we begin to increase our investment in Robotic Process Automation (RPA) and AI: the productivity of this (potentially) cheaper processing resource will matter – if not so much now then certainly when everyone is employing RPA to do similar tasks within the same services.”

But why would Robot B only do 500 tasks? They wouldn’t dawdle because they didn’t like their boss. They wouldn’t spend hours on social media, and they would surely only be allocated tasks that they were 100% capable of processing.

Maybe Robot B could only process 500 tasks because there were only 500 available to be done. Maybe the core system was running incredibly slowly that day, or there was so much network traffic that latency was affecting cycle times. Maybe someone changed a port on a firewall and the robot needed to be reset. Or there were hundreds of exceptions and the robot had to try them multiple times before rejecting them.

It is strange (isn’t it?) that if a person’s productivity is 50% we assume idleness, a propensity to waste time on social media, or a lack of skill but if it is a robot we quickly understand that it is the workflow that is the problem,” he continued.

Data-focused technologies such as Process Forensics and some digital operations management technologies or WFO technologies that seek to improve performance by URL logging or other screen monitoring techniques are totally missing the point: people’s productivity is far more influenced by the flow of work through the system than it is by their willingness to work or their skill level.

Workforce monitoring technologies seek to intimidate people into working harder, but you can’t intimidate people into having more work available to do. Equally, fluctuating demand, bottlenecks in the workflow, variations in work complexity will all drive variations in productivity – as with people, so it is with robots,” he added.

The answer is to introduce digital operations management solutions in the back office that will be the result of a blended human/RPA strategy made up of:

• Some degree of “demand smoothing” – using service level agreements to allow for carrying some work from one period to the next to even out resource requirements

• Some degree of cross-team collaboration – moving work or people between teams to balance demand and capacity.

The plain fact of the matter is that with humans and robotics increasingly working alongside one another in service operations a blended and balanced approach needs to be taken on the issue of productivity.

The financial services industry has witnessed considerable hype around artificial intelligence (AI) in recent months. We’re all seeing a slew of articles in the media, at conference keynote presentations and think-tanks tasked with leading the revolution. Below Sundeep Tengur, Senior Business Solutions Manager at SAS, explains how in the fight against fraud, AI is taking over as a dominant strategy, fuelled primarily by data.

AI indeed appears to be the new gold rush for large organisations and FinTech companies alike. However, with little common understanding of what AI really entails, there is growing fear of missing the boat on a technology hailed as the ‘holy grail of the data age.’ Devising an AI strategy has therefore become a boardroom conundrum for many business leaders.

How did it come to this – especially since less than two decades back, most popular references of artificial intelligence were in sci-fi movies? Will AI revolutionise the world of financial services? And more specifically, what does it bring to the party with regards to fraud detection? Let’s separate fact from fiction and explore what lies beyond the inflated expectations.

Why now?

Many practical ideas involving AI have been developed since the late 90s and early 00s but we’re only now seeing a surge in implementation of AI-driven use-cases. There are two main drivers behind this: new data assets and increased computational power. As the industry embraced big data, the breadth and depth of data within financial institutions has grown exponentially, powered by low-cost and distributed systems such as Hadoop. Computing power is also heavily commoditised, evidenced by modern smartphones now as powerful as many legacy business servers. The time for AI has started, but it will certainly require a journey for organisations to reach operational maturity rather than being a binary switch.

Don’t run before you can walk

The Gartner Hype Cycle for Emerging Technologies infers that there is a disconnect between the reality today and the vision for AI, an observation shared by many industry analysts. The research suggests that machine learning and deep learning could take between two-to-five years to meet market expectations, while artificial general intelligence (commonly referred to as strong AI, i.e. automation that could successfully perform any intellectual task in the same capacity as a human) could take up to 10 years for mainstream adoption.

Other publications predict that the pace could be much faster. The IDC FutureScape report suggests that “cognitive computing, artificial intelligence and machine learning will become the fastest growing segments of software development by the end of 2018; by 2021, 90% of organizations will be incorporating cognitive/AI and machine learning into new enterprise apps.”

AI adoption may still be in its infancy, but new implementations have gained significant momentum and early results show huge promise. For most financial organisations faced with rising fraud losses and the prohibitive costs linked to investigations, AI is increasingly positioned as a key technology to help automate instant fraud decisions, maximise the detection performance as well as streamlining alert volumes in the near future.

Data is the rocket fuel

Whilst AI certainly has the potential to add significant value in the detection of fraud, deploying a successful model is no simple feat. For every successful AI model, there are many more failed attempts than many would care to admit, and the root cause is often data. Data is the fuel for an operational risk engine: Poor input will lead to sub-optimal results, no matter how good the detection algorithms are. This means more noise in the fraud alerts with false positives as well as undetected cases.

On top of generic data concerns, there are additional, often overlooked factors which directly impact the effectiveness of data used for fraud management:

• Geographical variances in data.
• Varying risk appetites across products and channels.
• Accuracy of fraud classification (i.e. which proportion of the alerts marked as fraud are effectively confirmed ones).
• Relatively rare occurance of fraud compared to the huge bulk of transactions; having a suitable sample to train a model isn’t always guaranteed.

Ensuring that data meets minimum benchmarks is therefore critical, especially with ongoing digitalisation programmes which will subject banks to an avalanche of new data assets. These can certainly help augment fraud detection capabilities but need to be balanced with increased data protection and privacy regulations.

A hybrid ecosystem for fraud detection

Techniques available under the banner of artificial intelligence such as machine learning, deep learning, etc. are powerful assets but all seasoned counter-fraud professionals know the adage: Don’t put all your eggs in one basket.

Relying solely on predictive analytics to guard against fraud would be a naïve decision. In the context of the PSD2 (payment services directive) regulation in EU member states, a new payment channel is being introduced along with new payments actors and services, which will in turn drive new customer behaviour. Without historical data, predictive techniques such as AI will be starved of a valid training sample and therefore be rendered ineffective in the short term. Instead, the new risk factors can be mitigated through business scenarios and anomaly detection using peer group analysis, as part of a hybrid detection approach.

Yet another challenge is the ability to digest the output of some AI models into meaningful outcomes. Techniques such as neural networks or deep learning offer great accuracy and statistical fit but can also be opaque, delivering limited insight for interpretability and tuning. A “computer says no” response with no alternative workflows or complementary investigation tools creates friction in the transactional journey in cases of false positives, and may lead to customer attrition and reputational damage - a costly outcome in a digital era where customers can easily switch banks from the comfort of their homes.

Holistic view

For effective detection and deterrence, fraud strategists must gain a holistic view over their threat landscape. To achieve this, financial organisations should adopt multi-layered defences - but to ensure success, they need to aim for balance in their strategy. Balance between robust counter-fraud measures and positive customer experience. Balance between rigid internal controls and customer-centricity. And balance between curbing fraud losses and meeting revenue targets. Analytics is the fulcrum that can provide this necessary balance.

AI is a huge cog in the fraud operations machinery but one must not lose sight of the bigger picture. Real value lies in translating ‘artificial intelligence’ into ‘actionable intelligence’. In doing so, remember that your organisation does not need an AI strategy; instead let AI help drive your business strategy.